A pattern recognition system can be an Optical Character Recognition (OCR) system. OCR systems are known. They convert the image of text into machine-readable code by using a character recognition process. In an OCR system, the images of what could be characters are isolated and a character recognition process is used to identify the character.
Known optical character recognition processes generally comprise:                a normalization step that generates a normalized matrix from an input image;        a feature extraction step; and        a classification step to identify the character.        
The feature extraction step generates a feature vector that characterizes the input image and the classification step identifies the character starting from this feature vector. In some OCR processes, the feature extraction step involves a filtering with a Gabor filter. The choice of the Gabor filter is key for the OCR process because the Gabor filter determines the feature vector to identify the character. The feature vector has to contain the necessary information to identify the character with high accuracy. A too large feature vector makes the computation slow and a too small feature vector decreases the accuracy of the character identification. Known OCR processes using Gabor filters are too slow or have a too low accuracy. This is especially relevant for identification of Asian characters because of the extremely high number of Asian characters. Another disadvantage of the known Gabor filters is that they do not work adequately with the subsequent classification step.
U.S. Pat. No. 7,174,044B2 discloses a known method for character recognition based on Gabor filters that extracts the information of the specific directions of the characters. This method uses average on regions of Gabor filters and involves a lot of calculation and a large feature vector. This makes OCR processes using this method too slow.
The paper “High performance Chinese OCR based on Gabor features, discriminative feature extraction and model training” from Qiang Huo, Yong Ge and Zhi-Dan Feng in the Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2001, Volume 3, describes a known OCR process for Chinese characters. This method is fast but the recognition accuracy is not extremely high.
The dissertation called “Chinese OCR System Based on Gabor Features and SVM” from DaiWei at the Shanghai Jiaotong University describes another OCR process. SVM stands for “Support Vector Machine”, a supervised learning model that uses associated learning algorithms for data analysis and recognition algorithms. Such a SVM system requires a very large learning set that makes it either impractical or inaccurate.